The purpose of this R21 proposal is the development of a new nonlinear method that will be able to separate the dynamics of the sympathetic and parasympathetic nervous activities from noninvasive recordings of heart rate data. The cardiac autonomic nervous system (ANS) is an especially important control system that is responsible for maintaining [proper homeostasis of the cardiovascular system. Clinically-reliable assessment of the state of the ANS requires accurate nonlinear techniques that can separate the dynamics of sympathetic and parasympathetic nervous activities. Decoupling the dynamics of the two autonomic nervous activities based on heart rate data is important because it can be used as a powerful non-invasive marker for determining the state of the ANS balance. Experimental evidence suggests that myocardial infarction, chronic heart failure, ventricular tachycardia, and sudden cardiac death all exhibit signs of ANS imbalance. Currently, there is no method that can accurately characterize dynamics of the two branches of ANS using noninvasive approaches. One of the current standards in assessing the balance between the sympathetic and parasympathetic nervous systems is to compute the ratio of the low frequency (LF) to high frequency (HF) power obtained from spectral analyses of the heart rate data. The LF/HF ratio is inaccurate because it does not truly reflect the balance between the two branches of ANS activities, and is a linear approach despite the fact that the ANS involves nonlinear control. Consequently, characterization of the ANS using linear power spectra of the heart rate data may limit identification of subtle changes in dynamics from healthy to diseased states, for example. Preliminary results based on a limited database of healthy subjects suggest that our method is able to separate dynamics of the two ANS activities. The first aim of the R21 proposal is to further develop, modify, and enhance the capability of the method as the technique is applied to an existing clinical database to validate the efficacy of the approach. The second aim is to detect, quantify, and Interpret differences in dynamic characteristics of the ANS between normal and diseased subjects, in an attempt to find a marker for increased risk of sudden cardiac death. The final aim is to disseminate the developed software to the 9iomedical engineering community via the internet so that the algorithm can be tested with other researchers' own databases. Identifying and quantifying differences in the dynamic characteristics of ANS between normal and diseased conditions may lead to a better understanding of the role of the autonomic function imbalance in diseased conditions, and should have important clinical diagnostic and prognostic applications.